Beamforming Optimization in Distributed ISAC System With Integrated Active and Passive Sensing

In this paper, we study the transmit and receive beamforming vectors in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) performs the downlink communication with user equipments (UEs) and active sensing tasks simultaneously. While reflected signals are utilized for passive sensing at the receive access points (RAPs). We adopt different fusion strategies based on the backhaul capacity between the RAPs and BS. Specifically, in the scenarios with unlimited backhaul capacity, the sensing signals received by the BS and RAPs are forwarded to the central controller (CC) for signal fusion. In contrast, in the scenarios with limited backhaul capacity, the BS and each RAP make independent decisions and transmit their binary inference results to the CC for result fusion. Furthermore, we explore two cases of the signal-to-interference-plus-noise ratio (SINR) with and without the sensing interference cancellation (Case-1 SINR and Case-2 SINR). By optimizing the beamforming vectors according to different fusion strategies, we aim to maximize sensing performance while ensuring the minimum SINR requirement for the UEs subject to the power budget at the BS. Finally, numerical results demonstrate that the proposed beamforming optimization schemes can reach the upper bound of performance under both fusion strategies. It is also shown that adding the sensing signals generally improve the sensing performance in Case-1 SINR.